Clustering Behavior Research Articles

Do wild-caught fly larvae cooperatively forage?

Animals often form organized cooperative foraging groups, where individual members must adhere to specific rules to maintain cohesiveness. These groups face the challenge of managing potential intruders, who may or may not assist in foraging. In semi-liquid food environments, Drosophila larvae learn to synchronize their movements into clusters, which are thought to make feeding more efficient. Individuals who do not synchronize with the group are excluded from the cluster. Whether clustering behavior occurs in wild-caught larvae, and if so, the extent of their selectivity in group membership, remains unknown. Here, we show that clustering occurs across a number of fly species, and the capacity to join different clusters varies both between and within species. We collected and observed a larval cluster from rotting fruit in the field, yielding seven fly species. Subsequent tests for clustering on five lines from this collection and 20 other inbred wild-caught lines revealed that all species, except D. suzukii, exhibit clustering behavior. Each line demonstrates varying capacities to become members of different clusters. This study also indicates that there is high genetic variance in how individual lines cluster with each other that is not explained by cross species features. Additionally, combinations of wild species with lab benchmark strains give varied outcomes in resultant adult fitness. The ability to co-cluster varies between and within species boundaries. However, fly lines that cluster with another tend to impart fitness both to themselves and their host. Our findings demonstrate that multiple species of fly larvae can co-cluster. This behavior tends to confer mutual benefits to cluster members, suggesting significant ecological implications in Drosophila communities.

Spatiotemporal Cell Control via High-Precision Electronic Regulation of Microenvironmental pH.

Accurate regulation of extracellular pH is crucial for controlling cell behaviors and functions. However, typical methods, which primarily rely on replacing cell culture media or using ionic diffusion, are slow, nondirectional, and lack spatiotemporal resolution. Here, we develop a microfabricated device that regulates microenvironmental pH within specific localized zones with high precision (uncertainty <0.1 pH units) and temporal resolution. The device uses a synchronization strategy that coordinates two processes: pulsatile modulation of pH through microelectrolysis and ultrasensitive graphene-electronic pH sensing, which operates in antiphase to the modulation. Using this device, we show real-time control of the dynamic behaviors of microscale clusters of bacteria (motility) and cardiomyocytes (calcium signaling and necrotic injury) in response to precisely regulated extracellular pH variations. Our device addresses the limitations of typical pH-altering techniques and holds significant potential to advance cell biology, physiology, tissue engineering, and regenerative medicine.

Phase behavior and atomic dynamics in RbxNa1–x: insights from machine learning interatomic potentials based on ab initio molecular dynamics

Liquid alkali metal alloys have garnered significant attention because of their potential applications in coolant systems and batteries, driven by the need for environmental conservation and technological development. However, research on these complex systems is limited, necessitating a deeper understanding to ensure their safe and effective utilization. This study presents a comprehensive investigation of the factors that determine the phase diagram of RbxNa1-x. By reproducing the experimental results using the thermodynamic integration method and machine learning interatomic potentials based onab initiomolecular dynamics simulations, we uncovered the delicate balance between the energy and entropy contributions that influence the phase stability of these liquid metal alloys. Further analysis of the liquid phase revealed the crucial roles of volume and atomic mass. Additionally, the coordination numbers of the atoms revealed distinct clustering behaviors, where Na atoms tended to avoid proximity to other Na atoms, whereas Rb atoms exhibited a strong tendency to cluster together. Moreover, the diffusion dynamics further illustrated the asymmetry in the behavior of Rb and Na, with Rb showing increased diffusion at higher concentrations and Na exhibiting higher diffusion at lower concentrations. These findings offer significant insights into the phase stability and the dynamic and structural properties of these complex liquid metal alloys.

Measuring the intracluster medium velocity structure within the A3266 galaxy cluster

We present a detailed analysis of the velocity structure of the hot intracluster medium (ICM) within the A3266 galaxy cluster, including new observations taken between June and November 2023. Firstly, morphological structures within the galaxy cluster were examined using a Gaussian gradient magnitude (GGM) and an adaptively smoothed GGM filter applied to the EPIC-pn X-ray image. Then, we applied a novel XMM-Newton EPIC-pn energy scale calibration, which uses instrumental Cu Kalpha as a reference for the line emission, to measure the line-of-sight velocities of hot gas within the system. This approach enabled us to create two-dimensional projected maps for velocity, temperature, and metallicity, showing that the hot gas displays a redshifted systemic velocity relative to the cluster redshift across all fields of view. Further analysis of the velocity distribution through non-overlapping circular regions demonstrates consistent redshifted velocities extending up to 1125 kpc from the cluster core. Additionally, the velocity distribution was assessed along regions following surface brightness discontinuities, where we observed redshifted velocities in all regions, with the largest velocities reaching $768 km/s. Moreover, we computed the velocity probability density function (PDF) from the velocity map. We applied a normality test, finding that the PDF adheres to an unimodal normal distribution consistent with theoretical predictions. Lastly, we computed a velocity structure function for this system using the measured line-of-sight velocities. These insights advance our understanding of the dynamic processes within the A3266 galaxy cluster and contribute to our broader knowledge of ICM behavior in merging galaxy clusters.

Decomposition of Zero-Dimensional Persistence Modules via Rooted Subsets

AbstractWe study the decomposition of zero-dimensional persistence modules, viewed as functors valued in the category of vector spaces factorizing through sets. Instead of working directly at the level of vector spaces, we take a step back and first study the decomposition problem at the level of sets. This approach allows us to define the combinatorial notion of rooted subsets. In the case of a filtered metric space M, rooted subsets relate the clustering behavior of the points of M with the decomposition of the associated persistence module. In particular, we can identify intervals in such a decomposition quickly. In addition, rooted subsets can be understood as a generalization of the elder rule, and are also related to the notion of constant conqueror of Cai, Kim, Mémoli and Wang. As an application, we give a lower bound on the number of intervals that we can expect in the decomposition of zero-dimensional persistence modules of a density-Rips filtration in Euclidean space: in the limit, and under very general circumstances, we can expect that at least 25% of the indecomposable summands are interval modules.

Artefact design and societal worldview.

Technological artefacts are created in accordance with the values and worldviews of their designers. In operation, they act as a medium, facilitating and constraining human interaction with, and perception of, the world. When used on a large scale, they may lastingly affect societal ethos. If institutional structures of domination allocate the resources necessary for artefact design and development to some population groups over others, the direction and extent of such an effect may lead to increased disparity and inequity. While the direct influence of technology on opinion is well-studied, the evaluation of non-epistemic values, assumptions and presuppositions is a hurdle in the way of a deeper understanding of the large-scale effects of asymmetries in worldviews embodied by artefacts. Here, we show that artefacts have a strong potential to bias societal worldviews when they are distributed unevenly across the value spectrum. They can affect the clustering behaviour of agents with regard to worldview, both aiding and hindering intra- and inter-cluster diversity, depending on their distribution and frequency. Our findings underline the distributional sensitivity of worldview dynamics to institutional structures of domination. We highlight the importance of procedural interventions such as participatory design, which explicitly acknowledges existing asymmetries and redistributes power accordingly.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Understanding melting behavior of aluminum clusters using machine learned deep neural network potential energy surfaces.

Deep neural network-based deep potentials (DP), developed by Tuo et al., have been used to compute the thermodynamic properties of free aluminum clusters with accuracy close to that of density functional theory. Although Jarrold and collaborators have reported extensive experimental measurements on the melting temperatures and heat capacities of free aluminum clusters, no reports exist for finite-temperature abinitio simulations on larger clusters (N > 55 atoms). We report the heat capacities and melting temperatures for 32 clusters in the size range of 48-342 atoms, computed using the multiple histogram technique. Extensive molecular dynamics (MD) simulations at twenty four temperatures have been performed for all the clusters. Our results are in very good agreement with the experimental melting temperatures for 19 clusters. Except for a few sizes, the interesting features in the heat capacities have been reproduced. To gain insight into the striking features reported in the experiments, we used structural and dynamical descriptors such as temperature-dependent mean squared displacements and the Lindemann index. Bimodal features observed in Al116 and the weak shoulder seen in Al52 are attributed to solid-solid structural transitions. In confirmation of the earlier reports, we observe that the behavior of the heat capacities is significantly influenced by the nature of the ground state geometries. Our findings show that the sharp drop in the melting temperature of the 56-atom cluster is a consequence of the change in the geometry of Al55. Mulliken population analysis of Al55 reveals that the charge-induced local electric field is responsible for the strong bonding between core and surface atoms, leading to the higher melting temperature. Our calculations do not support the lower melting temperature observed in experimental studies of Al69. Our results indicate that Al48 is in a liquid state above 600K and does not support the high melting temperature reported in the experiment. It turns out that the accuracy of the DP model by Tuo et al. is not reliable for MD simulations beyond 750K. We also report low-lying equilibrium geometries and thermodynamics of 11 larger clusters (N = 147-342) that have not been previously reported, and the melting temperatures of these clusters are in good agreement with the experimental ones.

Information Propagation in Hypergraph-Based Social Networks.

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state (S) into active (Sa) and inactive (Si) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks.

Accelerated Simulations Reveal Physicochemical Factors Governing Stability and Composition of RNA Clusters.

Under certain conditions, RNA repeat sequences phase separate, yielding protein-free biomolecular condensates. Importantly, RNA repeat sequences have also been implicated in neurological disorders, such as Huntington's disease. Thus, mapping repeat sequences to their phase behavior, functions, and dysfunctions is an active area of research. However, despite several advances, it remains challenging to characterize the RNA phase behavior at a submolecular resolution. Here, we have implemented a residue-resolution coarse-grained model in LAMMPS─that incorporates both the RNA sequence and structure─to study the clustering propensities of protein-free RNA systems. Importantly, we achieve a multifold speedup in the simulation time compared to previous work. Leveraging this efficiency, we study the clustering propensity of all 20 nonredundant trinucleotide repeat sequences. Our results align with findings from experiments, emphasizing that canonical base-pairing and G-U wobble pairs play dominant roles in regulating cluster formation of RNA repeat sequences. Strikingly, we find strong entropic contributions to the stability and composition of RNA clusters, which is demonstrated for single-component RNA systems as well as binary mixtures of trinucleotide repeats. Additionally, we investigate the clustering behaviors of trinucleotide (odd) repeats and their quadranucleotide (even) counterparts. We observe that odd repeats exhibit stronger clustering tendencies, attributed to the presence of consecutive base pairs in their sequences that are disrupted in even repeat sequences. Altogether, our work extends the set of computational tools for probing RNA cluster formation at submolecular resolution and uncovers physicochemical principles that govern the stability and composition of the resulting clusters.

Investigation of the adsorption behaviors of novel Au3 cluster decorated arsenene nanosheets towards thiophene and methanethiol detection: A DFT study

First principles calculations were carried out to describe the interactions between Aun-arsenene nanosheets and methanethiol/thiophene molecules. Both Au-arsenene and Au3-arsenene nanosheets exhibit smaller band gap than the pure arsenene system due to the emergence of new energy bands near the Fermi level. Thus, the conductivity and sensor capabilities are significantly enhanced for the Au functionalized arsenene nanosheets. Calculated formation energies of −2.38 eV and −5.37 eV for Au-arsenene and Au3-arsenene nanosheets indicate their structural stability. The adsorption of methanethiol and thiophene molecules on the Au-arsenene substrates spontaneously occurs due to the negative calculated adsorption energies. Besides, thiophene molecule exhibits the highest adsorption energy (−0.52 eV) on the Au-arsenene substrate through its S atom strongly connected to the Au atom. The moderate adsorption energies indicate the shorter recovery times for sensing applications. Our work sheds light on the exploration of Au-arsenene as an effective candidate for sensing methanethiol and thiophene molecules.

Role of inclusion clusters on fatigue crack initiation in powder metallurgy nickel-based FGH96 superalloy

Inclusion clusters with a size of several tens of micrometers were found to be inclined to early crack initiation, resulting in an abnormally reduced fatigue life. Fatigue tests were conducted to elucidate crack initiation behaviors of inclusion clusters in nickel-based powder metallurgy (P/M) FGH96 superalloy. The inclusion clusters with varying sizes and shapes located at grain boundaries are composed of Al2O3, MgO and ZrO2 particles. The inclusion cluster with large size within 25-35 μm, small average particle distance within 0-4 μm and large orientation relative to loading direction within 35-66° will be more likely to cause crack initiation. In particular, the inclusion clusters with large size and high aggregation degree experience extruding during processing and subsequent heat-treatments due to the difference in thermal expansion coefficient with the matrix behave multiple cracking before fatigue loading, thereby facilitating crack initiation in the surrounding matrix grains. Moreover, as the main cracking part of inclusion clusters, the Al2O3 particles exhibit self-cracking and interface debonding, owing to higher hardness and poorer deformation coordination degree with matrix compared to other component inclusion particles.

Three-phase CFD-DEM study on the hydrodynamics of a riser system with liquid injection

Riser reactors are frequently applied in the process industry for highly important catalytic processes. In this work, a computational fluid dynamics- discrete element method (CFD-DEM) for a riser with liquid injection was developed. This model treats the gas as a continuous phase whereas the liquid droplets and catalyst particles are treated as discrete elements. The presence of droplets causes particles to be wet, which is taken into account via coverage models of the particles. In addition, the liquid on the particles will change the collision behavior, which is included using an energy balance approach. This CFD-DEM study is conducted simulating a lab-scale pseudo-2D riser. Based on the comparison, the assumption of fully covered particles results in a significant increase in the solids holdup and clustering behavior of the riser which, especially at the low liquid flow rate, results in an over-prediction. The partial coverage simulations show that only half of the particles are covered, which has a major effect on the collisions.

Atomic scale in-situ observation of gas-solid interaction regulating the pre-nucleation process of Pd atomic clusters

Advancements in nanotechnology have propelled the understanding of atomic-scale nucleation processes, essential for the evolution of atomic manufacturing. The process of amorphous precursors nucleation showcases a complex transition influenced by various factors. Utilizing aberration-corrected ETEM and theoretical calculation, we explore the nucleation of amorphous Pd atomic clusters. This study examines the nucleation dynamics and gas-solid interaction regulating of Pd clusters on ultrathin carbon films, prepared via electron beam evaporation. HRTEM observation and FFT analysis reveal that, in the early stage of nucleation, Pd cluster growth predominantly follows an Ostwald ripening-like mechanism. Atoms from smaller clusters migrate and attach to larger ones, facilitating their progression to critical nucleation size. Environmental conditions significantly influence this process; hydrogen atmospheres lower the surface energy of Pd clusters, reducing the critical nucleation size, while argon atmospheres impede growth of Pd clusters by occupying migration sites on the carbon surface. These insights into atomic cluster behavior and environmental interactions are crucial for understanding the early stages of nucleation from amorphous to crystalline and can help opens new avenues for the controlled fabrication of materials with optimized functionalities.

The Cumulation of Debris Clouds around a Fast-rotating Asteroid

Abstract The rotational mass loss has been realized to be a prevalent mechanism to produce low-speed debris near the asteroid, and the size composition of the asteroid’s surface regolith has been closely measured by in situ explorations. However, the full-scale evolution of the shedding debris has not been examined using the observed particle sizes, which may hold vital clues to the initial growth of an asteroid moonlet and help us to understand the general mechanisms that dominate the formation of asteroid systems. This paper presents our study on the cumulative evolution of the debris cloud formed by a rotationally unstable asteroid. A semianalytical model is developed to characterize the spatiotemporal evolution of the debris cloud posterior to a shedding event. Large-scale discrete element method (DEM) simulations are performed to quantify the clustering behavior of the debris particles in the mechanical environment near the asteroid. As a result, we found that the cumulation of a steady debris cloud is dominated by large pieces of debris, and the shedding particles follow a common migration trend, which fundamentally determines the mass distribution of the debris cloud. For the accretion analysis, we sketched the life cycle of a debris cluster and showed its dependency on particle size. The DEM simulations adopt physical parameters estimated from observations and asteroid missions. The results confirm that porous fluffy cluster structures can form shortly after a shedding event with magnitudes the same as the observed shedding activities. Measurements of these structures show that they themselves possess a certain strength and have the capacity to collisions to absorb dissociative particles that collide with them.

Mapping the interplay of work-arrangement, residential location, and activity engagement within an integrated model

Virtual activities, such as teleworking have been identified as major travel demand management strategies to tackle traffic congestion and emission. However, integrated models, which are capable of testing such strategies, have not yet been properly extended to capture emerging activity patterns. This study fills-up the literature gap by implementing individuals’ work-arrangement and introducing in-home (IH) virtual activities within an integrated Transport, Land-use, and Emission (iTLE) modeling framework. First, it conducts a household travel survey, which collected socio-economic, activity-travel, and work-related information through a questionnaire survey in the Halifax Regional Municipality (HRM), Nova Scotia, Canada. Next, it develops a work-arrangement choice model based on the mixed-logit modelling (MXL) approach and implements it within iTLE. After that, individuals’ daily activity programs are generated in a sequential manner utilizing a Markov Chain Monte Carlo (MCMC) modelling approach considering their work-arrangement, employment, and vehicle ownership. To demonstrate the application of the developed tool, individuals’ socio-demographics, residential-location, work-location, and activity-participation are longitudinally simulated up to 2031. The analysis of teleworking parameters revealed distinct clusters and individual preferences, emphasizing the significance of personalized approaches in formulating teleworking strategies. Most non-teleworkers stay closer to downtown than full-teleworkers while clustered behavior is observed among hybrid workers for residential location choice. Increase is observed for shopping, dining-out, and IH maintenance/discretionary activities while decrease is observed for out-of-home work. The outcomes of this paper will be helpful for policymakers and transport researchers to understand the evolution of work-arrangement and examine subsequent impacts on transportation and land-use systems.

In vitro assessment of assembly and sex pheromones in the control of larval stages of brown dog ticks

The increasing incidence of tick-borne diseases and the growing resistance to conventional acaricides underscore the urgent need for innovative and sustainable tick management strategies. This study investigates the potential of assembly and sex pheromones for the control of larval stages of Rhipicephalus sanguineus, the brown dog tick, which poses significant health risks to dogs. The ticks were collected from various canine hosts in Thrissur, Kerala and subjected to Petri-dish bioassays using vapour patches impregnated with synthetic assembly pheromone (AP) and sex pheromone (SP), both individually and in combination. The efficacy of these semiochemicals was assessed based on the attraction and clustering behaviour of unfed larvae and partially fed adult ticks over a 24-hour period. Results demonstrated a significantly higher attraction to AP-impregnated patches, with notable efficacy observed in causing tick aggregation, particularly beyond 30 min of exposure. While SP also showed potential in attracting ticks, the combination of SP and AP did not result in a synergistic effect. The study highlights the potential of using semiochemicals as an environmentally sustainable alternative to traditional acaricides, highlighting their role in integrated pest management strategies Keywords: Rhipicephalus sanguineus, adenine, guanine, xanthine, 2,6-DCP, Petri-dish bioassay

Influence of temperature gradients on helium diffusion and clustering in tungsten: A molecular dynamics study

Tungsten and its alloys, used as plasma-facing materials in fusion reactors, endure high-flux, low-energy helium ion irradiation and temperature gradients induced by thermal load and shocks. This study utilizes molecular dynamics (MD) simulations to explore the diffusion and clustering behavior of helium (He) in tungsten (W) under temperature gradients. The migration behavior of isolated helium atoms, small helium clusters, and helium self-interstitial atom (He-SIA) clusters within tungsten is analyzed. It is revealed that both helium and He-SIA clusters tend to migrate towards higher temperature regions, exhibiting negative thermophoresis. The nucleation of helium clusters, the formation of Frenkel pairs, and the interactions between self-interstitial clusters and helium clusters are also investigated. The results indicate that helium concentration significantly impacts He nucleation behavior. Directional diffusion may lead to areas of high concentration over time, potentially promoting the formation of He clusters and bubbles. These insights enhance the understanding of tungsten's performance and durability as a plasma-facing component in fusion reactors.

Clustering of RNA Polymerase II C-Terminal Domain Models upon Phosphorylation.

RNA polymerase II (Pol II) C-terminal domain (CTD) is known to have crucial roles in regulating transcription. CTD has also been highly recognized for undergoing phase separation, which is further associated with its regulatory functions. However, the molecular interactions that the CTD forms to induce clustering to drive phase separations and how the phosphorylation of the CTD affects clustering are not entirely known. In this work, we studied the concentrated solutions of two heptapeptide repeat (2CTD) models at different phosphorylation patterns and protein and ion concentrations using all-atom molecular dynamics simulations to investigate clustering behavior and molecular interactions driving the cluster formation. Our results show that salt concentration and phosphorylation patterns play an important role in determining the clustering pattern, specifically at low protein concentrations. The balance between inter- and intrapeptide interactions and counterion coordination together impact the clustering behavior upon phosphorylation.

A Comparative Analysis Of Collaborative Filtering Models For Game Recommendation Using Cosine Similarity, SVD, K-Means Clustering And Real-Time Game Insights

This paper presents a study on GameRS, a game recommendation system that employs collaborative filtering techniques, including Cosine Similarity, Singular Value Decomposition (SVD), and K-means. Clustering, in conjunction with real-time game insights facilitated by Groq AI. The system integrates data from the RAWG API to provide game recommendations and dynamically retrieves game details, including genres, platforms, reviews, ratings, release dates, trailers, and gameplay mechanics. Furthermore, it presents a novel User Satisfaction Index for Games (USIG), a metric designed to assess anticipated enjoyment by considering factors such as rating, genre similarity, and platform similarity. Users may pose specific inquiries related to games, to which Groq AI provides succinct and accurate answers. GameRS, developed with Streamlit, manages both the front-end interface and back-end logic, allowing users to access recommendations, game details, trailers, and additional features. Evaluation results indicate that Cosine Similarity surpasses alternative methods regarding recall and hit rate, whereas SVD and K-means provide insights into latent user preferences and clustering behaviors.

Super-resolution microscopy unveils the nanoscale organization and self-limiting clustering of CD47 in human erythrocytes.

The transmembrane protein CD47, an innate immune checkpoint protein, plays a pivotal role in preventing healthy erythrocytes from immune clearance. Our study utilized stochastic optical-reconstruction microscopy (STORM) and single-molecule analysis to investigate the distribution of CD47 on the human erythrocyte membrane. Contrary to previous findings in mouse erythrocytes, we discovered that CD47 exists in randomly distributed monomers rather than in clusters across the human erythrocyte membrane. Using 2nd antibody-induced crosslinking, we found that CD47 aggregates into stable clusters within minutes. By comparing these STORM results with those of the fully mobile protein CD59 and the cytoskeleton-bound membrane protein glycophorin C under similar conditions, as well as devising two-color STORM co-labeling and co-clustering experiments, we further quantitatively revealed an intermediate, self-limiting clustering behavior of CD47, elucidating its fractional (∼14%) attachment to the cytoskeleton. Moreover, we report reductions in both the amount of CD47 and its clustering capability in aged erythrocytes, providing new insight into erythrocyte senescence. Together, the combination of STORM and 2nd antibody-based crosslinking unveils the unique self-limiting clustering behavior of CD47 due to its fractional cytoskeleton attachment.